Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Yinyang Gongji Pills have the effects of strengthening the body resistance to eliminate pathogenic factors, removing stasis, and reducing swelling, which is a commonly used traditional Chinese medicine(TCM) formula for treating intestinal accumulation. A real-world, registered, and single-arm clinical trial was conducted to observe the clinical efficacy and safety of Yinyang Gongji Pills combined with capecitabine in the treatment of advanced colorectal cancer and analyze the clinical characteristics of the patients. A total of 60 patients with advanced colorectal cancer who refused or could not tolerate standard treatment of western medicine were included in the study. They were treated with Yinyang Gongji Pills combined with capecitabine until disease progression or intolerable adverse events occurred. The main observation indicators were progression-free survival(PFS) and safety. The treatment effects of the patients under different baseline characteristics were analyzed. The clinical trial has found that the median PFS of all enrolled patients was 7.3 months, with 30.1% of patients having a PFS exceeding 12.0 months. Layered analysis showed that the median PFS of patients with the onset site being the colon and rectum were respectively 8.4 and 4.7 months. The median PFS of patients with high, medium, and low tumor burden were respectively 7.0, 4.7, and 10.8 months. The median PFS of patients with wild-type and mutant-type RAS/BRAF were respectively 7.9 and 6.9 months. The median PFS of patients with KPS scores ≥80 and ≤70 were respectively 7.9 and 6.5 months. The median PFS of patients treated with Yinyang Gongji Pills for ≥6, 3-6, and ≤3 months were respectively 8.0, 5.2, and 4.2 months. The median PFS of patients with spleen, kidney, liver, and lung syndrome differentiation in TCM were respectively 8.3, 6.7, 7.3, and 5.6 months. The median PFS of patients with TCM pathological factors including phlegm, dampness, and blood stasis were respectively 7.0, 7.3, and 6.5 months. Common adverse reactions include anemia, decreased white blood cells, decreased appetite, fatigue, and hand foot syndrome, with incidence rates being respectively 44.2%, 34.6%, 42.3%, 32.7%, and 17.3%. The results showed that the combination of Yinyang Gongji Pills and capecitabine demonstrated potential clinical efficacy and good safety in this study. The patients have clinical characteristics such as low tumor burden, onset site at the colon, KPS scores ≥ 80, long duration of oral TCM, and TCM syndrome differentiation including spleen or liver.
Humans ; Capecitabine/adverse effects* ; Colorectal Neoplasms/mortality* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Middle Aged ; Female ; Aged ; Adult ; Treatment Outcome

Aim To verify the therapeutic effect of the Qushi Huoxue decoction(QSHXF)on a mouse model of non-alcoholic fatty liver disease(NAFLD)using network pharmacology and experimental approaches,to examine the changes in the PI3K-AKT-lipid metabo-lism signaling pathway,and to elucidate its molecular mechanisms.Methods The potential active ingredi-ents and targets of the QSHXF were identified using the TCMSP platform.NAFLD-related genes were sourced from the GeneCards,PharmGkb,TTD,and OMIM data-bases.The intersection of drug targets and NAFLD treatment targets was analyzed to identify the key tar-gets of the QSHXF in treating NAFLD.The STRING database and Cytoscape 3.9.1 software were utilized to construct networks linking traditional Chinese medicine active ingredients to disease targets and PPI networks,allowing for the screening of key active ingredients and core targets.GO and KEGG enrichment analyses of the intersecting targets were conducted using R version 4.2.2.The NAFLD model was established by feeding mice a methionine-choline deficient diet for a duration of five weeks.Following successful modeling,low,me-dium,and high doses of the QSHXF were administered for intervention over a period of six weeks.The efficacy was verified and the underlying mechanisms were ex-plored using methods such as HE staining,Oil Red O staining,and Western blot analysis.Results The net-work pharmacology prediction indicated that QSHXF might effectively treat NAFLD through key components such as quercetin and kaempferol,as well as core tar-gets including STAT3,AKT1,and HIF1A.KEGG en-richment analysis further suggested that QSHXF might exert its therapeutic effects on NAFLD via signaling pathways such as AGE-RAGE and PI3K-AKT.Verifi-cation through animal experiments demonstrated that QSHXF could significantly reduce hepatic steatosis and lipid droplet accumulation in NAFLD mice.Specifical-ly,it markedly decreased serum levels of TC,TG,ALT,AST,and LDL,while increasing HDL levels.Addition-ally,the treatment significantly reduced the protein ex-pression levels of p-PI3K,p-AKT,SREBP-1c,FASN,and ACC1 in the liver.Conclusions QSHXF can sig-nificantly enhance liver function,improve blood lipid levels,and alleviate hepatic steatosis in NAFLD mice,with its mechanism potentially linked to the inhibition of the PI3K-AKT-lipid metabolism signaling pathway.

Aim To explore the in vitro anti-human triple-negative breast cancer(TNBC)effect and mech-anism of Austocystin R.Methods MTT assay was used to evaluate the anti-tumor potential of Austocystin R for various human tumor cells and normal cells.Flow cytometry was employed to evaluate the influence on cell cycle progression.mRFP-GFP-LC3 adenovirus transfection was used to evaluate the autophagic flux process.Western blot assay was used to verify the effect of Austocystin R on the expression of related pro-teins.Results The results showed that Austocystin R significantly inhibited the proliferation of multiple tumor cells in a dose-dependent manner,especially for the MDA-MB-231 cells with an IC50 of 1.45μmol·L-1.In addition,Austocystin R increased the protein expression of PTEN,p53,p-p53,p27,p21,and down-regulated the expression of p-PI3K,p-AKT and p-mTOR.Austocystin R can significantly increase the proportion of S-phase MDA-MB-231 cells,inhibit the expression of Cyclin D1,CDK4,CDK6,Rb,Cyclin B1 and CDK1,and promote the expression of Cyclin E1 and CDK2.Austocystin R can promote the autophagic flux process of MDA-MB-231 cells,promote the expres-sion of LC3 Ⅰ/Ⅱ,p-Beclin-1,p-ULK1,HMGB-1 and Atg 14 proteins,and inhibit the expression of Beclin-1,ULK1,p62,ATG 3,ATG 4B,ATG 5,ATG 7,ATG 12,ATG 13 and ATG 16L1 proteins.Conclusion Austo-cystin R can exhibit its anti-TNBC activity by inhibi-ting the PI3K/AKT/mTOR signaling pathway,blocking the cell cycle at the S phase and inducing autophagic cell death.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon gene (STING) signaling pathway is a crucial component of the immune system. It detects abnormal cytosolic double-stranded DNA and promotes the expression of type I interferons and other inflammatory factors, thereby protecting the body from pathogenic infections. In children, an immature immune system or genetic mutations can lead to immune dysregulation, increasing the risk of autoimmune diseases (AID) and autoinflammatory diseases. Recent studies have shown that aberrant activation of the cGAS-STING signaling pathway is associated with the development of AID and autoinflammatory diseases in children. This review summarizes the research progress on the cGAS-STING signaling pathway in childhood AID and autoinflammatory diseases, aiming to provide new directions for clinical diagnosis and treatment.
Humans ; Nucleotidyltransferases/physiology* ; Membrane Proteins/physiology* ; Signal Transduction/physiology* ; Child ; Autoimmune Diseases/immunology* ; Inflammation/etiology*

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies